Immobilization as a powerful bioremediation tool for abatement of dye pollution: a review
Bibliographic record
Abstract
Dyes are xenobiotic compounds widely used by textile, leather, paper, printing, food, pharmaceutical, and cosmetic industries. Decolorization and dye degradation in the effluents is a prime hurdle in its treatment, and there is still a shortage of economically attractive and easy-to-operate treatments that can eliminate dye pollution. In recent years, chemical-based treatments are being replaced by greener technologies at the laboratory and industrial scale to combat dye pollution. It is noteworthy that immobilization is a biotechnological tool that greatly enhances bioremediation’s potential. The present review has covered the basic concepts of immobilization, including the different immobilization techniques and the various carriers used for immobilization. The efficient immobilization of a biocatalyst depends on the proper choice of a carrier combined with a suitable immobilization technique. Hence, this review provides a comparative analysis of the different immobilization techniques and carriers used. Further, there is an in-depth discussion on the potential of immobilized enzymes and cells as bioremediation agents for dye degradation. Nearly all the studies indicated that immobilization enhanced the biodecolorization of colored wastewater compared with free systems. Further, the potential of immobilized systems for large scale industrial implementation was also examined. The article ends with a note on the loopholes of research on immobilization and future scopes of this technique.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".